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Could Have Been Email

by io.github.pipeworx-io

Polymarket Arbitrage

polymarket_arbitrage
Read-onlyIdempotent

Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks. Call with NO args for a trending_scan of the top ~200 markets by weekly volume; pass event for the strongest per-event partition_check, or topic for a themed cross-event scan. event (recommended for a specific market): pass a Polymarket event slug like "fed-decision-may-2026" or "when-will-bitcoin-hit-150k"; walks child markets, checks date-axis / threshold-axis ordering AND computes the partition_check (sum of YES prices across mutually-exclusive legs — should ≈1; deviations >3pp emit a BUY/SELL EVERY LEG signal). topic (for cross-event scanning): pass a seed question like "Strait of Hormuz traffic returns to normal" or "Fed rate decision"; searches related events across the platform, flattens markets, runs the comparator on the union. Cross-event mode catches "...by May 31" vs "...by Jun 30" patterns that single-event misses. SEMANTIC ANCHOR: cross-event pairs require ≥0.30 Jaccard similarity on question tokens (prevents Powell-Fed-Pause being paired with Powell-DOJ-probe); skipped_low_similarity surfaces the rejected pair count. PARTITION FILTER: drops will-person-X / will-manager-Y / will-someone-else- placeholder slugs; partitions with >20% placeholder fraction return null arb signal. Response: opportunities[] (gap_pp, suggested_trade, reasoning, monotonicity violation context), and in event mode partition_check{sum_yes_prices, gap_from_1, placeholders_filtered, suggested_trade}. FILL CHECK: when the partition signal fires, arbitrage.fill_check prices it against live CLOB depth (theoretical_edge_pp_at_book vs realizable_edge_pp at 1000 shares/leg, thin_legs[]) — realizable_edge_pp ≤ 0 means the overround exists only at last-trade, not in the book; do not trade it. For custom sizing use polymarket_fill_risk.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
eventNoSingle-event mode (use this if you know the specific Polymarket event): event slug like "fed-decision-may-2026" or "when-will-bitcoin-hit-150k". Full Polymarket URLs also accepted.
topicNoCross-event mode (use this if you want to scan related events across the platform): a topic or seed question like "Fed rate decision" or "Strait of Hormuz traffic returns to normal". Tool searches Polymarket for related events and checks monotonicity across them.

TDQS

A4.7/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Provides extensive behavioral detail beyond annotations: semantic anchor (Jaccard ≥0.30), partition filter (placeholder >20% → null), fill check comparing theoretical vs realizable edge with specific threshold (realizable ≤0 → do not trade). Annotations only indicate readOnly, openWorld, idempotent, non-destructive; description adds crucial algorithmic and output behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is lengthy but well-structured with clear paragraphs for each mode, semantic anchor, partition filter, fill check, and response format. A few sentences could be tightened (e.g., repeating 'use this if you know'), but overall efficient for the tool's complexity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (multiple modes, edge cases, fill check), the description covers all critical aspects: input parameter semantics, algorithmic logic, filtering conditions, output structure, and trade guidance (when not to trade). No output schema is provided, but description compensates fully. Annotations and schema enrich the picture.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with descriptions for both parameters. Description adds significant context: event accepts full URLs, topic is a seed question, and explains how each mode affects scanning scope (single-event vs cross-event). Substantially enhances schema meaning.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states the tool finds arbitrage opportunities on Polymarket via monotonicity violations and partition-sum checks, with three distinct operating modes (no args, event, topic). Differentiates itself from sibling tools like polymarket_edges and polymarket_fill_risk by its specific function.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly describes when to use each parameter: 'Call with NO args for a trending_scan', 'event (recommended for a specific market)', 'topic for cross-event scanning'. Does not directly contrast with sibling tools but provides clear usage context. Lacks explicit when-not-to-use guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A3.9/5.0
Disambiguation2/5

Several tool clusters are nearly indistinguishable in purpose: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route to the same underlying catalog, and the six polymarket tools heavily overlap in surfacing prediction-market edge. Even with detailed descriptions, an agent could easily misselect between bet_research and polymarket_edges or between discover_tools and suggest_questions.

Naming Consistency3/5

Most names use lowercase snake_case, but the pattern is mixed: some are verb_noun (compare_entities, resolve_entity), some are bare verbs (remember, forget, recall), and some are compound noun phrases (polymarket_edges, pipeworx_trending). ask_pipeworx also breaks the separator convention compared to ask_pipeworx_beta and ask_pipeworx_grounded.

Tool Count2/5

With 32 tools, this exceeds the 25+ threshold for 'too many' and feels like a platform bundle rather than a focused server. It spans data querying, prediction markets, memory, subscriptions, feedback, AI visibility, dependency scanning, and llms.txt generation, which is far more surface area than one coherent server should present.

Completeness4/5

For the core data-research and prediction-market domains, coverage is strong: query, grounded verification, deep research, entity resolution, comparisons, change feeds, arbitrage, fill-risk, subscriptions, and memory are all present with no major dead ends. The gaps are mostly the single-purpose oddballs (could_have_been_email_analyze, generate_llms_txt, scan_dependency) that don't connect to the rest of the surface.

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